in the field of artificially intelligent computer systems capable of answering questions posed in natural language, cognitive question answering (QA) systems (such as the IBM Watson™ artificially intelligent computer system or and other natural language question answering systems) process questions posed in natural language to determine answers and associated confidence scores based on knowledge acquired by the QA system. In operation, users submit one or more questions through a front-end application user interface (UI) or application programming interface (API) to the QA system where the questions are processed to generate answers that are returned to the user(s). When a large number of users are simultaneously submitting questions, such as during distress situations, the submitted questions typically do not include explicit information identifying each user, resulting in the submitted questions being treated independently from one another by the QA system which generates separate procedural responses from the ingested corpus. As a result, traditional QA systems will sequentially process submitted questions in chronological order without regard to user identity information or any contextual understanding of other, potentially relevant questions and/or answers. As a result, the existing solutions for efficiently providing contextualized processing of questions and answers are extremely difficult at a practical level.